CN116696323A - Early warning device and method for drilling process - Google Patents
Early warning device and method for drilling process Download PDFInfo
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- CN116696323A CN116696323A CN202310870752.2A CN202310870752A CN116696323A CN 116696323 A CN116696323 A CN 116696323A CN 202310870752 A CN202310870752 A CN 202310870752A CN 116696323 A CN116696323 A CN 116696323A
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- 238000005553 drilling Methods 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 50
- 239000000523 sample Substances 0.000 claims abstract description 105
- 239000011159 matrix material Substances 0.000 claims abstract description 51
- 238000003062 neural network model Methods 0.000 claims abstract description 19
- 230000001133 acceleration Effects 0.000 claims description 35
- 238000004891 communication Methods 0.000 claims description 25
- 239000011435 rock Substances 0.000 claims description 4
- 238000009825 accumulation Methods 0.000 claims description 3
- 238000012847 principal component analysis method Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 description 8
- 239000013598 vector Substances 0.000 description 8
- 238000011176 pooling Methods 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 230000002238 attenuated effect Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
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- 239000011800 void material Substances 0.000 description 1
- 208000008918 voyeurism Diseases 0.000 description 1
Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B10/00—Drill bits
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B17/00—Drilling rods or pipes; Flexible drill strings; Kellies; Drill collars; Sucker rods; Cables; Casings; Tubings
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/12—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geophysics (AREA)
- Mechanical Engineering (AREA)
- Remote Sensing (AREA)
- Earth Drilling (AREA)
Abstract
The application discloses a drilling process early warning device and a drilling process early warning method. The early warning device comprises a first vibration sensor, at least two second vibration sensors and a computer terminal; the first vibration sensor acquires first vibration data during drilling; at least two second vibration sensors acquire second vibration data during drilling; the computer constructs a vibration data matrix according to the first vibration data and at least two second vibration data; and the computer terminal predicts the drilling state associated with the vibration data matrix according to at least one neural network model. The embodiment discloses early warning device based on the signal characteristics that the drill rod generates vibration and attenuates along the drill rod direction when the drill rod reaches and does not reach the deep empty zone, utilizes the neural network model to predict the signal characteristics so as to complete the prediction of whether the current drilling reaches the deep empty zone on the earth surface, and helps the intelligent probe rod to develop a sensor of the intelligent probe rod only after reaching the deep empty zone, thereby avoiding equipment damage.
Description
Technical Field
The application relates to the field of geological exploration, in particular to a drilling process early warning device and method.
Background
The peeping imaging of the deep empty area is that a measurer holds a detection instrument deep into an underground space to perform detection, and the detection instrument is sent into the deep empty area to perform detection through drilling.
For the existing detecting instrument, such as sonar, laser radar, camera device, etc., a stable drilling channel must be formed in advance, so that the detecting instrument can enter the deep empty area to perform detection, but the detecting depth of the detecting instrument is limited, and the detection in the deep empty area cannot be performed.
Then, for the deep goaf formed by deep coal mining, no matter the goaf is a caving behind a working face or a rescue site of an underground accident, a measuring person cannot enter, and as the overlying strata of the deep goaf are severely damaged by mining disturbance, an effective drilling channel cannot be formed on the ground, and a detecting instrument cannot be fed into the deep goaf. In emergency rescue work, because of the time and the task weight, personnel measurement and drilling detection in the deep empty area cannot meet the accident handling requirements.
Therefore, various detection devices synchronously enter the deep empty areas along with the geological drill bit, and the operation while drilling is implemented, so that the method is a key technology for detecting the deep empty areas currently.
During the drilling operation, after the drill bit reaches the deep empty area, the sensor combination part of the intelligent probe rod needs to be unfolded, so that the sensor combination can acquire real-time data in the deep empty area. However, the data in the deep empty area cannot be collected before the sensor is unfolded, so that the sensor cannot help to judge whether the intelligent probe rod reaches the deep empty area, and the equipment is easily damaged if the sensor is unfolded before the intelligent probe rod reaches the deep empty area.
Disclosure of Invention
The embodiment of the application discloses a drilling process early warning device and a drilling process early warning method for overcoming the defects in the prior art.
In a first aspect, an embodiment of the application discloses a drilling process early warning device, which is applied to a drill rod system; the drill rod system comprises a drill bit, an intelligent probe rod and a communication drill rod, wherein the intelligent probe rod is vertically connected between the drill bit and the communication drill rod; the early warning device comprises a first vibration sensor, at least two second vibration sensors and a computer terminal; the first vibration sensor is deployed on the intelligent probe rod, and acquires first vibration data fed back by the drill bit at the position of the intelligent probe rod during drilling; at least two second vibration sensors are vertically arranged on the communication drill rod, and second vibration data fed back by the drill bit at the position of the communication drill rod during drilling are obtained; the computer terminal is deployed on the ground surface and receives the first vibration data and the second vibration data through the communication drill rod; the computer terminal constructs a vibration data matrix according to the first vibration data and at least two second vibration data; the computer terminal predicts the drilling state associated with the vibration data matrix according to at least one neural network model; and the computer terminal judges the state that the drill bit reaches the deep empty area according to the drilling state.
In addition, in the embodiment of the present application, the computer terminal acquires a first vibration acceleration of the first vibration data along the drilling direction; the computer terminal acquires a second vibration acceleration of the second vibration data along the drilling direction; and the computer terminal constructs the vibration data matrix according to the first vibration acceleration and at least two second acceleration data.
In addition, in the embodiment of the present application, the computer terminal acquires first frequency domain data of the first vibration acceleration, and second frequency domain data of the second vibration acceleration; the computer terminal acquires first amplitude points of different frequency components in the first frequency domain data, and second amplitude points of different frequency components in the second frequency domain data; the computer terminal creates the vibration data matrix according to the first amplitude point and at least two second amplitude points.
In addition, in the embodiment of the present application, the computer terminal acquires the first vibration acceleration as the first frequency domain data according to a fast fourier transform.
In addition, in the embodiment of the present application, the computer terminal acquires the second vibration acceleration as the second frequency domain data according to a fast fourier transform.
In addition, in the embodiment of the application, the vibration data matrix of the computer terminal is a dimension-reducing data matrix through a principal component analysis method; the computer terminal predicts the drilling status associated with the reduced dimension data matrix according to the neural network model.
In addition, in the embodiment of the application, the neural network model is configured with the computer terminal to acquire first sample data fed back by the drill bit at the position of the intelligent probe rod when the drill bit reaches the deep empty zone; the computer terminal acquires at least two second sample data fed back by the drill bit at the position of the communication drill rod when the drill bit reaches the deep empty area; the computer terminal acquires a first sample acceleration and a second sample acceleration of the first sample data and the second sample data along the drilling direction; the computer terminal acquires first sample frequency domain data of the first sample acceleration and second sample frequency domain data of the second sample acceleration; the computer terminal acquires a first sample amplitude point and a second sample amplitude point of the first sample frequency domain data and the second sample frequency domain data; the computer terminal creates a sample data matrix according to the first sample amplitude point and the second sample amplitude point; the computer terminal is marked with a sample label associated with the sample data matrix; the computer terminal trains the neural network model through at least one of the sample data matrices and the data set of the sample tags.
In addition, in the embodiment of the application, the computer terminal simulates the first sample data fed back by the drill bit at the intelligent probe rod position when the drill bit reaches the deep empty area through simulation, and communicates the second sample data fed back by the drill rod.
In addition, the sample label in the embodiment of the application at least comprises one or more of a deep empty zone, a deep water accumulation zone and a deep broken rock zone.
In a second aspect, an embodiment of the present application discloses a drilling process early warning method, where the early warning method is applied to the early warning device, and the early warning method includes that the first vibration sensor obtains first vibration data fed back by the drill bit at the position of the intelligent probe rod during drilling; at least two second vibration sensors acquire second vibration data fed back by the drill bit at the position of the communication drill rod during drilling; the computer terminal deploys and receives the first vibration data and the second vibration data; the computer terminal constructs a vibration data matrix according to the first vibration data and at least two second vibration data; the computer terminal predicts the drilling state associated with the vibration data matrix according to at least one neural network model; and the computer terminal judges the state that the drill bit reaches the deep empty area according to the drilling state.
Compared with the prior art, the embodiment of the application discloses the early warning device, which predicts the signal characteristics of vibration generated by the drill rod when the drill rod reaches the deep empty zone and the vibration generated by the drill rod before the drill rod reaches the deep empty zone and the signal characteristics of vibration generated by the drill rod when the drill rod does not reach the deep empty zone and the vibration generated by the drill rod and the signal characteristics of the drill rod attenuated by the drill rod along the length direction of the communication drill rod in the drilling process by utilizing the neural network model so as to predict whether the current drilling reaches the deep empty zone or not on the earth surface, so that the intelligent probe rod can unfold a sensor of the intelligent probe rod after the intelligent probe rod reaches the deep empty zone, and damage to equipment in the sensor combination is avoided.
Other features of embodiments of the present application and advantages thereof will be apparent from the following detailed description of the disclosed exemplary embodiments with reference to the drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a drilling process early warning device deployed in a drill pipe system according to the present embodiment;
FIG. 2 is a schematic flow chart of predicting that the intelligent probe rod reaches the deep empty area according to the embodiment;
fig. 3 is a flow chart illustrating the process of acquiring frequency domain data according to the present embodiment.
Detailed Description
In order that the application may be readily understood, a more complete description of the application will be rendered by reference to the appended drawings. Embodiments of the application are illustrated in the accompanying drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are disclosed in order to provide a thorough and complete disclosure of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," and/or the like, specify the presence of stated features, integers, steps, operations, elements, components, or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.
The embodiment discloses an early warning device for a drilling process. The drilling process early warning device is applied to a drill rod system. The drill rod system comprises a drill bit 110, an intelligent probe rod 120, a communication drill rod 130 and a relay rod section 140; the intelligent probe 120 is vertically connected between the drill bit 110 and the communication drill rod 130.
Fig. 1 shows a schematic structural diagram of the early warning device of the embodiment deployed in a drill pipe system. Fig. 1 shows that the early warning system includes a first vibration sensor, a plurality of second vibration sensors, and a computer terminal. A first vibration sensor is disposed on the intelligent probe 120 for acquiring first vibration data of the vibration sensor near the deployment location of the intelligent probe 120 in real time as the drill bit 110 drills. The plurality of second vibration sensors are disposed at intervals along the rod directions of the communication drill rods 130, specifically, the second vibration sensors are disposed on the relay rod sections 140 between two adjacent communication drill rods 130 respectively, and are used for acquiring second vibration data of the second vibration sensors near the deployment positions of the relay rod sections 140 in real time when the drill bit 110 drills. The computer terminal is deployed at the surface and reads the electrical signals from the communication link within the communication drill pipe 130 via the cable, whereby the computer terminal receives the first vibration data and the second vibration data via the communication link and predicts whether the portion of the intelligent probe 120 has reached the deep space and its associated area based on the combination of the first vibration data and the second vibration data.
Fig. 2 is a schematic flow chart of predicting whether the intelligent probe 120 reaches the deep empty area according to vibration data by the computer terminal in the embodiment. FIG. 2 shows that the computer terminal predicts whether the intelligent probe 120 reaches the deep empty area based on the vibration data, including the following steps.
Step S10 the computer terminal receives first vibration data and a plurality of second vibration data from the formation along communication drill pipe 130.
And step S20, the computer terminal constructs a vibration data matrix according to the first vibration data and the plurality of second vibration data.
Step S30, the computer terminal predicts the drilling state associated with the vibration data matrix according to a previously trained neural network model.
And step S40, the computer terminal judges the state that the drill bit 110 reaches the deep empty area according to the drilling state, wherein the state that the drill bit 110 reaches the deep empty area comprises any one of the deep empty area, the deep water accumulation area and the deep broken rock area.
In the embodiment, in the technical scheme of step S10, the computer terminal acquires frequency domain data of the vibration acceleration. Fig. 3 shows a flow chart of acquiring frequency domain data by the computer terminal according to the present embodiment. Fig. 3 shows that the acquisition of frequency domain data by a computer terminal comprises the following steps.
Step S11, the computer terminal acquires first vibration acceleration of the first vibration data and second vibration acceleration of the second vibration data along the drilling direction.
And step S12, the computer terminal constructs the vibration data matrix according to the first vibration acceleration and the plurality of second acceleration data.
And step S13, the computer terminal acquires the first vibration acceleration as first frequency domain data and the second vibration acceleration as second frequency domain data according to the fast Fourier transform.
In the technical solution of step S20, the computer terminal obtains a first amplitude point set of a plurality of frequency components in the first frequency domain data, a second amplitude point set of a plurality of frequency components in the second frequency domain data, and sequentially arranges the first amplitude point set and the plurality of second amplitude point sets in an order of setting up each device from the stratum to create the vibration data matrix.
Further, in step S20, the computer terminal in this embodiment reduces the dimension of the vibration data matrix by the principal component analysis method to a dimension-reduced data matrix. The computer terminal obtaining the reduced-dimension data matrix includes the following steps.
Step S21 the computer terminal obtains a vibration data matrix, wherein the vibration data matrix includes all amplitude points of the drill bit 110 during at least one successive drilling cycle.
Wherein the first set of amplitude points has 1 and is arranged in the top row. The second set of amplitude points has N-1 and a plurality of rows arranged in a bottom-to-top deployment order of the formation. Each set of amplitude points selects the first M amplitude points, whereby the vibration data moment is,
step S22 calculates a covariance matrix C of the vibration data matrix X.
The general covariance formula is given by the formula, is the characteristic mean value.
Preferably, wherein X, Y samples and when the covariance formula is positive, it is stated that X and Y are positive correlations; when the covariance formula is negative, it is indicated that X and Y are negative correlations, and when the covariance is 0, X and Y are independent of each other.
Step S23 obtaining all feature pointsFeature mean.
Step S24 is according toThe feature mean value zero-equalizes all feature points of the vibration data matrix X, even if +.>Is->All 0.
The simplified covariance formula is given by the formula,
step S25 since the covariance of the vibration data matrix X is a symmetric square matrix, then
Further the covariance formula is given by the formula,
step S26, the embodiment carries out singular value decomposition on the covariance matrix C, and calculates eigenvalues and eigenvectors of the covariance matrix C.
Step S27, constructing a dimension reduction matrix P according to the eigenvalues and the eigenvectors.
Step S28, according to the magnitude of the characteristic values, sequentially arranging the characteristic vectors corresponding to the characteristic values;
step S29 establishes a feature vector matrix Z from the aligned feature vectors.
Step S210 selects the first K rows of the eigenvector matrix Z to construct a dimension-reduction matrix P, wherein K is smaller than N and is a positive integer.
Step S211 calculates the product of the vibration data matrix X and the dimension-reduction matrix P as the dimension-reduced dimension-reduction matrix.
In the technical solution of step S30, the selection of the neural network model by the computer terminal includes the following steps.
S31, the computer terminal acquires first sample data fed back by the drill bit 110 at the position of the intelligent probe 120 when the drill bit reaches a deep space; at least two second sample data are obtained for the bit 110 as it reaches the deep void, which is fed back at the position of the communication drill pipe 130.
Preferably, the computer terminal simulates the first sample data fed back by the drill bit 110 at the intelligent probe 120 position when the deep space is reached, and communicates the second sample data fed back by the drill rod 130.
S32, the computer terminal acquires a first sample acceleration and a second sample acceleration of the first sample data and the second sample data along the drilling direction.
S33, the computer terminal acquires first sample frequency domain data of the first sample acceleration and second sample frequency domain data of the second sample acceleration;
s34, the computer terminal acquires a first sample amplitude point and a second sample amplitude point of the first sample frequency domain data and the second sample frequency domain data;
s35, the computer terminal creates a sample data matrix according to the first sample amplitude point and the second sample amplitude point;
the computer terminal in S36 is marked with a sample tag associated with the sample data matrix, where the sample tag is preferably classified into a plurality of types, such as a deep empty zone, a deep water-logging zone, a deep broken rock zone, and the like.
The computer terminal trains the neural network model through at least one of the sample data matrices and the data set of the sample tags S37.
In the technical solution of step S30, the neural network model in this embodiment is configured as a convolutional neural network model. In the technical solution of step S30, in the embodiment, the neural network model is configured to configure the number of convolutional layers and the number and sizes of feature graphs applied by all the convolutional layers for the convolutional neural network model; the convolution layer A has 16 deconvolution feature vectors, each feature vector is deconvolved by using a convolution kernel of 11 multiplied by 1, the moving step length is set to be 2, and the layer outputs 32 feature vectors with the size of 93 multiplied by 1; the convolution layer B adopts a 9 multiplied by 1 convolution kernel and outputs 32 eigenvectors with the size of 85 multiplied by 1; the pooling layer a adopts a 3 multiplied by 1 pooling core, the moving step length is 2, and 32 eigenvectors with the size of 42 multiplied by 1 are generated; the convolution layer C adopts a convolution kernel of 7 multiplied by 1, the moving step length is 2, and 32 convolution kernels with the size of 18 multiplied by 1 are output; the convolution layer D adopts a convolution kernel of 5 multiplied by 1, and outputs 32 feature vectors of 14 multiplied by 1; the pooling layer b adopts a 3 multiplied by 1 pooling core, the moving step length is 2, and 32 eigenvectors with the size of 6 multiplied by 1 are generated; the convolution layer E uses a convolution kernel of 6×1, and outputs 32 feature vectors of 1×1 size.
Further, the embodiment discloses a drilling process early warning method. The early warning method is applied to the early warning device. The early warning method in this embodiment includes that a vibration sensor obtains first vibration data fed back by the drill bit 110 at the position of the intelligent probe 120 during drilling; at least two second vibration sensors acquire second vibration data that is fed back by drill bit 110 at the position of communication drill pipe 130 while drilling. The computer terminal deploys and receives the first vibration data and the second vibration data; and the computer terminal is provided with a vibration data matrix according to the first vibration data and at least two second vibration data. The computer terminal predicts the drilling state associated with the vibration data matrix according to the at least one neural network model, and judges the state of the drill bit 110 reaching the deep empty area according to the drilling state.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment.
Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (10)
1. A pre-warning device for a drilling process,
the early warning device is applied to a drill rod system;
the drill rod system comprises a drill bit, an intelligent probe rod and a communication drill rod;
the intelligent probe rod is vertically connected between the drill bit and the communication drill rod;
it is characterized in that the method comprises the steps of,
the early warning device comprises a first vibration sensor, at least two second vibration sensors and a computer terminal;
the first vibration sensor is deployed on the intelligent probe rod, and acquires first vibration data fed back by the drill bit at the position of the intelligent probe rod during drilling;
at least two second vibration sensors are vertically arranged on the communication drill rod, and second vibration data fed back by the drill bit at the position of the communication drill rod during drilling are obtained;
the computer terminal is deployed on the ground surface and receives the first vibration data and the second vibration data through the communication drill rod;
the computer terminal constructs a vibration data matrix according to the first vibration data and at least two second vibration data;
the computer terminal predicts the drilling state associated with the vibration data matrix according to at least one neural network model;
and the computer terminal judges the state that the drill bit reaches the deep empty area according to the drilling state.
2. The drilling process warning device of claim 1, wherein,
the computer terminal acquires a first vibration acceleration of the first vibration data along the drilling direction;
the computer terminal acquires a second vibration acceleration of the second vibration data along the drilling direction;
and the computer terminal constructs the vibration data matrix according to the first vibration acceleration and at least two second acceleration data.
3. The drilling process warning device of claim 1, wherein,
the computer terminal acquires first frequency domain data of the first vibration acceleration and second frequency domain data of the second vibration acceleration;
the computer terminal acquires first amplitude points of different frequency components in the first frequency domain data, and second amplitude points of different frequency components in the second frequency domain data;
the computer terminal creates the vibration data matrix according to the first amplitude point and at least two second amplitude points.
4. The apparatus and method for early warning of a drilling process according to claim 3, characterized in that,
and the computer terminal acquires the first vibration acceleration as the first frequency domain data according to the fast Fourier transform.
5. The apparatus and method for early warning of a drilling process according to claim 3, characterized in that,
and the computer terminal acquires the second vibration acceleration as the second frequency domain data according to the fast Fourier transform.
6. The apparatus and method for early warning of a drilling process according to claim 3, characterized in that,
the computer terminal reduces the dimension by a principal component analysis method, and the vibration data matrix is a dimension reduction data matrix;
the computer terminal predicts the drilling status associated with the reduced dimension data matrix according to the neural network model.
7. The apparatus and method for early warning of a drilling process according to claim 3, characterized in that,
the neural network model is configured with,
the computer terminal acquires first sample data fed back by the drill bit at the intelligent probe rod position when the drill bit reaches a deep empty area;
the computer terminal acquires at least two second sample data fed back by the drill bit at the position of the communication drill rod when the drill bit reaches the deep empty area;
the computer terminal acquires a first sample acceleration and a second sample acceleration of the first sample data and the second sample data along the drilling direction;
the computer terminal acquires first sample frequency domain data of the first sample acceleration and second sample frequency domain data of the second sample acceleration;
the computer terminal acquires a first sample amplitude point and a second sample amplitude point of the first sample frequency domain data and the second sample frequency domain data;
the computer terminal creates a sample data matrix according to the first sample amplitude point and the second sample amplitude point;
the computer terminal is marked with a sample label associated with the sample data matrix;
the computer terminal trains the neural network model through at least one of the sample data matrices and the data set of the sample tags.
8. The apparatus and method for early warning of a drilling process according to claim 4, characterized in that,
the computer terminal simulates the first sample data fed back by the drill bit at the intelligent probe rod position when the drill bit reaches the deep empty area through simulation, and communicates the second sample data fed back by the drill rod.
9. The apparatus and method for early warning of a drilling process according to claim 4, characterized in that,
the sample tag comprises at least one or more of a deep empty zone, a deep water accumulation zone and a deep broken rock zone.
10. A method for early warning in the drilling process,
the early warning method is applied to the early warning device for the drilling process according to claim 1,
it is characterized in that the method comprises the steps of,
the pre-warning method comprises the steps of,
the first vibration sensor acquires first vibration data fed back by the drill bit at the intelligent probe rod position during drilling;
at least two second vibration sensors acquire second vibration data fed back by the drill bit at the position of the communication drill rod during drilling;
the computer terminal deploys and receives the first vibration data and the second vibration data;
the computer terminal constructs a vibration data matrix according to the first vibration data and at least two second vibration data;
the computer terminal predicts the drilling state associated with the vibration data matrix according to at least one neural network model;
and the computer terminal judges the state that the drill bit reaches the deep empty area according to the drilling state.
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